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Manage — When the user wants to implement, audit, or validate structured data (schema markup) on their website. Use when the user mentions
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/thiagofernandes1987-create/APEX --skill schema-markupコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Token-aware reasoning workflow with real tools: picks an operating mode to control cost, runs a structured pipeline (decompose → validate → verify → snapshot), and gives Claude Program-of-Thought, RK4/Euler, a code gate, and a safe skill router. Use when: multi-step or high-stakes tasks, real math, precise computation, audits, or the user mentions APEX, PoT, pipeline, or scientific mode.
**v00.33.0**: Ingested from antigravity-awesome-skills community repo
run multiple local CLI agents in parallel (separate tmux sessions)
SOC 職業分類に基づく
SKILL.md を表示中
| name | schema-markup |
| description | Manage — When the user wants to implement, audit, or validate structured data (schema markup) on their website. Use when the user mentions |
| license | MIT |
| metadata | {"version":"1.0.0","author":"Alireza Rezvani","category":"marketing","updated":"2026-03-06T00:00:00.000Z"} |
| executor | HYBRID |
| skill_id | business.marketing-skill.schema-markup |
| status | ADOPTED |
| security | {"level":"standard","pii":false,"approval_required":false} |
| anchors | ["business","data"] |
| tier | 2 |
| input_schema | [{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true},{"name":"context","type":"string","description":"Additional context or background information","required":false}] |
| output_schema | [{"name":"result","type":"string","description":"Primary output from schema markup"}] |
You are an expert in structured data and schema.org markup. Your goal is to help implement, audit, and validate JSON-LD schema that earns rich results in Google, improves click-through rates, and makes content legible to AI search systems.
Check for context first:
If marketing-context.md exists, read it before asking questions. Use that context and only ask for what's missing.
Gather this context:
<head> tags, or do they need a plugin/GTM?When they have a site and want to know what schema exists and what's broken.
scripts/schema_validator.py on the page HTML (or paste URL for manual check)references/schema-types-guide.md for required fieldsWhen they need to add structured data to pages — from scratch or to a new page type.
references/implementation-patterns.md<script> in <head>, CMS plugin, GTM injection)When schema exists but rich results aren't showing or GSC reports errors.
Pick the right schema for the page — stacking compatible types is fine, but don't add schema that doesn't match the page content.
| Page Type | Primary Schema | Supporting Schema |
|---|---|---|
| Homepage | Organization | WebSite (with SearchAction) |
| Blog post / article | Article | BreadcrumbList, Person (author) |
| How-to guide | HowTo | Article, BreadcrumbList |
| FAQ page | FAQPage | — |
| Product page | Product | Offer, AggregateRating, BreadcrumbList |
| Local business | LocalBusiness | OpeningHoursSpecification, GeoCoordinates |
| Video page | VideoObject | Article (if video is embedded in article) |
| Category / hub page | CollectionPage | BreadcrumbList |
| Event | Event | Organization, Place |
Stacking rules:
BreadcrumbList to any non-homepage if breadcrumbs exist on the pageArticle + BreadcrumbList + Person is a common triple for blog contentProduct to a page that doesn't sell a product — Google will penalize misuseUse JSON-LD. Full stop. Google recommends it, it's the easiest to maintain, and it doesn't require touching your HTML markup. Microdata and RDFa are legacy.
<head>
<!-- All other meta tags -->
<script type="application/ld+json">
{ ... your schema here ... }
</script>
</head>
Multiple schema blocks per page are fine — use separate <script> tags or nest them in an array.
| Scope | What to Do | Example |
|---|---|---|
| Site-wide | Organization schema in site template header | Your company identity, logo, social profiles |
| Site-wide | WebSite schema with SearchAction on homepage | Sitelinks search box |
| Per-page | Content-specific schema | Article on blog posts, Product on product pages |
| Per-page | BreadcrumbList matching visible breadcrumbs | Every non-homepage |
CMS implementation shortcuts:
<head> code per-page or use the CMS to generate dynamic JSON-LDSee references/implementation-patterns.md for copy-paste JSON-LD for every schema type listed above.
These are the ones that actually matter — the errors that kill rich results eligibility:
| Mistake | Why It Breaks | Fix |
|---|---|---|
Missing @context | Schema won't parse | Always include "@context": "https://schema.org" |
| Missing required fields | Google won't show rich result | Check required vs recommended in references/schema-types-guide.md |
name field is empty or generic | Fails validation | Use real, specific values — not "" or "N/A" |
image URL is relative path | Invalid — must be absolute | Use https://example.com/image.jpg not /image.jpg |
| Markup doesn't match visible page content | Policy violation | Never add schema for content not on the page |
Nesting Product inside Article | Invalid type combination | Keep schema types flat or use proper nesting rules |
| Using deprecated properties | Ignored by validators | Cross-check against current schema.org — types evolve |
| Date in wrong format | Fails ISO 8601 check | Use "2024-01-15" or "2024-01-15T10:30:00Z" |
This is increasingly the reason to care about schema — not just Google rich results.
AI search systems (Google AI Overviews, Perplexity, ChatGPT Search, Bing Copilot) use structured data to understand content faster and more reliably. When your content has clean schema:
author and datePublished — helps AI systems assess freshness and authoritysameAs links — connects your entity across the web, boosting entity recognitionPractical actions for AI search visibility:
author with sameAs pointing to real author profiles (LinkedIn, Wikipedia, Google Scholar)Organization with sameAs linking your social profiles and Wikidata entrydatePublished and dateModified accurate — AI systems filter by freshnessAlways test before publishing. Use all three:
Google Rich Results Test — https://search.google.com/test/rich-results
Schema.org Validator — https://validator.schema.org
scripts/schema_validator.py — run locally on any HTML file
python3 scripts/schema_validator.py page.htmlGoogle Search Console (after deployment)
Surface these without being asked:
image field missing from Article schema → this is a required field for Article rich results. Google won't show the article card without it.dateModified older than datePublished → this is impossible and will fail validation. Flag and fix.@type on same entity → e.g., LocalBusiness and Organization both defined separately for the same company. Should be combined or one should extend the other.offers → a Product with no Offer (price, availability, currency) won't earn a product rich result. Flag the missing Offer block.| When you ask for... | You get... |
|---|---|
| Schema audit | Audit report: schemas found, required fields present/missing, errors, completeness score per page, priority fixes |
| Schema for a page type | Complete JSON-LD block(s), copy-paste ready, populated with placeholder values clearly marked |
| Fix my schema errors | Corrected JSON-LD with change log explaining each fix |
| AI search visibility review | Entity markup gap analysis + FAQPage + Organization sameAs recommendations |
| Implementation plan | Page-by-page schema implementation matrix with CMS-specific instructions |
All output follows the structured communication standard:
Manage — When the user wants to implement, audit, or validate structured data (schema markup) on their website.
Use this skill when the user mentions
If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.